Warmly’s person-level intent platform makes marketing more precise by identifying ideal customers, monitoring their buying intent in real time, and engaging through the right channel at the right moment. The product depends on constant small improvements that add up to a great customer experience.

The Challenge

Customers often share small but important requests, UX tweaks, bug fixes, and feature refinements, that improve day-to-day usability. Like many growth-stage teams, Warmly struggled to get those issues prioritized. 

Maximus Greenwald, co-founder and CEO, said:

“I’m not able to code fast enough to appease my customers, and many many tickets are not worked on purely because of engineering bandwidth.”

Product and engineering were focused on major roadmap items, so low-friction fixes sat in the backlog even when the aggregate impact was big.

Bringing Codegen Into the Workflow

Warmly invited Codegen into their workspace to change that equation. Codegen CEO, Jay Hack, showed the team how to work efficiently with AI agents and encouraged them to ship ten features in a single day to demonstrate what’s possible. 

The goal was to leave Warmly with a teammate that is a fraction of the cost and 10× the productivity of a typical engineer.

For the first time, customer service managers (CSMs) acted as product managers and junior developers. They identified problems, described solutions in natural language, and saw those solutions run live in production — all within hours.

One big customer request was to push a new feature to be able to drag, drop, and reorder the chat buttons on the front end. The CSM wrote a product requirement in Linear, asked Codegen to draft the technical spec, and an engineer only needed to confirm class names and function signatures before merge.

Greenwald states:

“Codegen is our Slack teammate that allows our CS team to interact with engineering and our codebase in a way that saves the engineering team tons of time and actually moves the needle on smaller features and bug requests that we never would have gotten to otherwise.”

Results

CSMs as PMs

Customer-facing teammates can now move directly from a user request to a live solution without blocking on engineering bandwidth.

Carina Boo, co-founder and Head of CS notes:

“A CSM basically can act as a product manager and then pair with an engineer and actually get stuff done. We have a ton of PRs already out to staging and we’re going to be deploying later today.”

Faster Feature Delivery

Warmly built a customer health app in about four hours, which replaced a $20,000 third-party tool and weeks of expected engineering time. They were also able to ship 30 features into production in a single day.

Stephanie Merlis, CSM, stated: 

“This feature probably would have taken a full day to get shipped. But thanks to Codegen, we were able to do it in under an hour.”

10× Engineering Leverage

Codegen effectively gave every engineer a fleet of junior developers, enabling more PRs, quicker bug fixes, and continuous background improvements without hiring more staff.

Looking Ahead

Warmly proved that AI agents can expand who gets to build software. CSMs now function as an extension of engineering, delivering customer-driven fixes and entirely new applications in a fraction of the usual time.

Greenwald concluded:

“Before today, I thought the best way to solve engineering bottlenecks was to hire more engineers. Now I realize that I can use Codegen to save money on additional engineers, and empower my existing engineers to be 10x more effective.”

Ready to see what Codegen can do for your company? Try for free or reach out to our team for a demo.

The post Case Study: How Warmly’s CSMs Ship Production Features with Codegen appeared first on The Codegen Blog.

The team needed a way to reduce overhead, automate routine changes, and free engineers to focus on solving real problems — all without bolting on another tool that disrupted their workflow.

Lambda Curry co-founder, Jake Ruesink creates new issues in Linear, then drive progress with updates, questions, and threaded discussions.

Why Codegen

From the start, Codegen provided more than code generation. Acting as a 24/7 engineer and project manager inside Slack, GitHub, and Linear, it supported every step of the process:

• Developing new components and API endpoints
• Applying small changes instantly without disrupting active work
• Explaining complex functions for easier debugging and refactoring
• Looking up dependencies and internal references in seconds
• Managing tasks with structured tracking inside Linear

Context-Rich Development

Lambda Curry used Codegen, and embedded AI-powered engineering directly into their existing stack. The team used Codegen in three distinct ways:

1. In Slack: Developers ask questions, debug tricky functions, and request code changes directly in chat.
2. In GitHub: Codegen automatically reviews code, generates suggestions, and opens pull requests.
3. In Linear: Tasks stay aligned with engineering work, and Codegen helps to create structured issues and track progress.

This meant Lambda Curry could trigger PRs, assign tasks, and resolve questions instantly — without jumping between systems.

Jake Ruesink links GitHub PRs & commits to Linear issues, post updates, and spin up follow-up tasks fast.

Real-World Impact

With Codegen, Lambda Curry transformed how its developers work day-to-day:

• Thanks to Linear + Codegen, routine tasks are handled automatically, while higher-impact work gets engineers’ full attention.
• Codegen generates the first draft of new components or endpoints, letting engineers focus on refining functionality instead of starting from scratch.
• Configuration updates and style tweaks happen instantly, without derailing deep work.
• Complex functions get explained on demand, simplifying debugging and refactoring.

Jake Ruesink, Lambda Curry co-founder, said:

“It helps us go from task planning to implementation with astonishing speed. Sometimes what we thought would take hours now takes minutes. It’s become hard to estimate timelines — in the best way.”

Moving Forward

Lambda Curry proves what’s possible when you bring Codegen into your workflow: AI that reshapes how teams review, build, and ship.

Want results like this? Try Codegen for free or reach out to our team for a demo.

The post Customer Success Story: Lambda Curry appeared first on The Codegen Blog.

But what if an AI tool could handle the heavy lifting for you? Today we’ll go through a case study on how Codegen’s AI can safely automate your migration from SQLAlchemy 1.6 to 2.0.

Why Upgrade to 2.0?

SQLAlchemy 2.0 makes your code cleaner, less error-prone, and more maintainable. By upgrading, you get streamlined ORM functionality for query handling and APIs, AsyncIO support, type annotations, removal of implicit aliasing, and more.

But manually upgrading to 2.0 is hard. Numerous APIs and ORM methods have been deprecated or changed. Overlooking even a small detail can cause messy runtime errors.

How Codegen Automates the Migration

1. With our proprietary static analysis software, we index your entire codebase and build a map of how everything depends on each other.
2. Our AI refactors your code to use the latest APIs and patterns from SQLAlchemy 2.0. Thanks to the static analysis, we ensure that changes are comprehensive and won’t break either upstream or downstream dependencies.
3. We run your unit tests and lint rules against the changes to ensure there were no errors or regressions.

Case Study: Codegen’s Automated Migration

This Github repo shows a before-and-after comparison of a Codegen-assisted SQLAlchemy 2.0 migration for a simple book-tracking app. The migration guide breaks it down into a series of Codemods, such as this one that updates all files to adhere to SQLAlchemy2.0 class inheritance protocol:

Here are some highlighted diffs from the complete migration.

Removing implicit autocommit

Updating query syntax to use where() and select() constructs

Using new relationship loading techniques

Personally, I’m getting a migraine just thinking about making all these updates by hand, even for a simple CRUD app like this.

Set up a demo to get started with Codegen today!

The post Case Study: AI-Powered SQLAlchemy Upgrade appeared first on The Codegen Blog.